For tabular in-context learning models, recourse is well-defined, its cost is bounded and converges to classical linear recourse as context grows; ASR-ICL finds sparse recourse with fewer queries.
Learning decision trees and forests with algorithmic recourse
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Algorithmic Recourse of In-Context Learning for Tabular Data
For tabular in-context learning models, recourse is well-defined, its cost is bounded and converges to classical linear recourse as context grows; ASR-ICL finds sparse recourse with fewer queries.